Summary¶
When providing the same input parameters and units, class and classy has the same outputs.
In [1]:
import os
import io
from pathlib import Path
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import pandas as pd
import numpy as np
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import matplotlib.pyplot as plt
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%matplotlib inline
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import plotly.offline as py
py.init_notebook_mode()
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import holoviews as hv
hv.extension('plotly')
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IDX = pd.IndexSlice
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%load_ext autoreload
%autoreload 2
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from dautil.plot import iplot_column_slider, plot_column_slider
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def read_txt(path):
with open(path, 'r') as f:
# remove beginning `#`
text = f.read()[1:]
with io.StringIO(text) as f:
# input has 6 sig. fig. which fits in float32
return pd.read_csv(f, delim_whitespace=True, index_col=0, dtype=np.float32)
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def read_class_txt(path, camb=False):
# read once
with open(path, 'r') as f:
text = f.read()
# get last comment line
comment = None
for line in text.split('\n'):
if line.startswith('#'):
comment = line
# remove beginning '#'
comment = comment[1:]
# parse comment line: get name after ':'
names = [name.split(':')[1] for name in comment.strip().split()]
with io.StringIO(text) as f:
df = pd.read_csv(f, delim_whitespace=True, index_col=0, comment='#', header=None, names=names)
df.columns.name = 'spectra'
return df if camb else df * 1.e12
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def plot_compare(df1, df2, keys, log=False, relative=False):
'''plot to compare columns from `df1` and `df2`,
using columns from `df1`.
`keys`: tuple of str of names of `df1` and `df2`.
`relative`: if True, plot the relative error w.r.t. `df1` instead.
'''
for col in df1.columns:
# pd.merge(df1[col], df2[col], left_index=True, right_index=True).plot()
df_temp = pd.concat((df1[col], df2[col]), axis=1, join='inner', keys=(' '.join((key, col)) for key in keys))
if relative:
temp = df_temp.values
if log:
plt.loglog(df_temp.index, np.abs((temp[:, 0] - temp[:, 1]) / temp[:, 0]), label=col)
else:
plt.plot(df_temp.index, np.abs((temp[:, 0] - temp[:, 1]) / temp[:, 0]), label=col)
plt.legend()
plt.show()
else:
if log:
df_temp.plot(logx=True, logy=True)
else:
df_temp.plot()
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# first value is default
# rest are alternative value for each realization
PARAMS = {
'STELLAR_BARYON_FRAC': (0.05, 0.001, 1.),
'STELLAR_BARYON_PL': (0.5, -0.5, 1.),
'ESC_FRAC': (0.01, 0.001, 1.),
'ESC_PL': (-0.5, -1., 0.5),
'M_TURNOVER': (5e8, 1e8, 1e10),
't_STAR': (0.5, 0., 1.),
'L_X': (40.5, 38., 42.)
}
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def parse_case(string):
for level, idx in enumerate(map(int, string.split('_'))):
if idx != 0:
break
if idx == 0:
return 'default'
else:
param = list(PARAMS)[level]
return '{}={:.6}'.format(param, PARAMS[param][idx])
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basedir = Path('~/git/source/UCB-PHYS229-2019Spring-final-project/output').expanduser()
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df_path = pd.DataFrame(basedir.glob('*-cl_lensed.dat'), columns=['path'])
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df_path['case'] = df_path.path.map(lambda path: path.name.split('-')[0])
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df_path['name'] = df_path.case.map(parse_case)
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df_path
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Class result from command line¶
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df = pd.concat(
(read_class_txt(path, camb=True) for path in df_path.path),
axis=1,
keys=df_path.name
# keys=df_path.case
)
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df.columns.names = ('case', 'spectra')
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plot_column_slider(df, slider=True)
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for spectrum in df.columns.levels[1]:
py.plot(iplot_column_slider(df.T.loc[IDX[:, spectrum], :].T), filename=f'../docs/media/{spectrum}.html')
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for spectrum in df.columns.levels[1]:
print(f'{{width=100% height=500}}', end='\n\n')